Specifies the kernel type to be used in the algorithm.
It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or
a callable.
If none is given, ‘rbf’ will be used. If a callable is given it is
used to precompute the kernel matrix.

degree:int, optional (default=3)

Degree of the polynomial kernel function (‘poly’).
Ignored by all other kernels.

gamma:float, optional (default=’auto’)

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

Current default is ‘auto’ which uses 1 / n_features,
if gamma='scale' is passed then it uses 1 / (n_features * X.var())
as value of gamma. The current default of gamma, ‘auto’, will change
to ‘scale’ in version 0.22. ‘auto_deprecated’, a deprecated version of
‘auto’ is used as a default indicating that no explicit value of gamma
was passed.

coef0:float, optional (default=0.0)

Independent term in kernel function.
It is only significant in ‘poly’ and ‘sigmoid’.

tol:float, optional

Tolerance for stopping criterion.

nu:float, optional

An upper bound on the fraction of training
errors and a lower bound of the fraction of support
vectors. Should be in the interval (0, 1]. By default 0.5
will be taken.

shrinking:boolean, optional

Whether to use the shrinking heuristic.

cache_size:float, optional

Specify the size of the kernel cache (in MB).

verbose:bool, default: False

Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in libsvm that, if enabled, may not work
properly in a multithreaded context.

Deprecated since version 0.20: random_state has been deprecated in 0.20 and will be removed in
0.22.

Attributes:

support_:array-like, shape = [n_SV]

Indices of support vectors.

support_vectors_:array-like, shape = [nSV, n_features]

Support vectors.

dual_coef_:array, shape = [1, n_SV]

Coefficients of the support vectors in the decision function.

coef_:array, shape = [1, n_features]

Weights assigned to the features (coefficients in the primal
problem). This is only available in the case of a linear kernel.

coef_ is readonly property derived from dual_coef_ and
support_vectors_

intercept_:array, shape = [1,]

Constant in the decision function.

offset_:float

Offset used to define the decision function from the raw scores.
We have the relation: decision_function = score_samples - offset_.
The offset is the opposite of intercept_ and is provided for
consistency with other outlier detection algorithms.

The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter> so that it’s possible to update each
component of a nested object.